电测与仪表2018，Vol.55Issue(2)：45-51,7.
基于VMD-SE-IPSO-BNN的超短期风电功率预测
Ultra short-term wind power forecasting based on VMD-SE-IPSO-BNN
摘要
Abstract
The accurate prediction of wind power is of great importance for large scale wind power connecting to the grid.In order to predict the wind speed more accurately,a combined model based on variational mode decompositionsample entropy (VMD-SE) and Bayesian neural network optimized by improved particle swarm optimization (IPSO) is proposed for ultra short-term wind power prediction.Firstly,the wind power time sequence was decomposed into a series of wind speed sub-modes with different bandwidths to reduce its non-linearity by using VMD-SE.Then,the Bayesian neural network is established for all sub-modes,and the weights and thresholds of the Bayesian neural network are optimized by IPSO to obtain the optimal prediction results.Simulation results demonstrate that the forecasting model based on VMD-SE has higher prediction accuracy than other conventional decomposition methods.The proposed combined prediction model has higher prediction accuracy.关键词
超短期风电功率预测/可变模式分解/样本熵/改进粒子群算法/贝叶斯神经网络/预测精度Key words
ultra short-term wind power forecasting/variational mode decomposition/sample entropy/improved particle swarm optimization/Bayesian neural network/prediction accuracy分类
信息技术与安全科学引用本文复制引用
殷豪,董朕,孟安波..基于VMD-SE-IPSO-BNN的超短期风电功率预测[J].电测与仪表,2018,55(2):45-51,7.基金项目
广东省科技计划项目(2016A010104016) （2016A010104016）
广东电网公司科技项目(GDKJQQ20152066) （GDKJQQ20152066）
